> Markdown version of [/jobs/ext/2735936-data-platform-engineer](https://www.wearedevelopers.com/jobs/ext/2735936-data-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Platform Engineer - **Company:** ADONIS INC. - **Location:** New York, United States - **Experience:** Experienced - **Salary:** $165,000.0 - $190,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Validation, Information Engineering, Data Warehousing, Software Debugging, Python (Programming Language), Raw Data, SQL Databases, Datadog, Snowflake, Production Code - **Published:** September 5, 2026 - **Apply:** https://startup.jobs/data-platform-engineer-adonis-9841868 ## About the Role * 2+ years of software or data engineering experience with production code you can walk through in detail. * Solid Python and SQL fundamentals. You may not have deep warehouse experience yet, but you understand how data moves through a system. * Exposure to at least one part of the modern data stack: a cloud warehouse, an orchestration tool, or a transformation framework. * Strong debugging instincts and honesty about what you know versus what you are guessing. * You respond well to feedback and probing - depth may still be developing, but the fundamentals and the trajectory are visible. * Interest in healthcare data. You do not need domain experience, but you need the appetite for a messy, regulated domain. Nice to Have * Snowflake, SQLMesh/dbt, Temporal, or AWS exposure. * Any prior contact with healthcare, claims, or other regulated data domains. ## Description You will build and operate components of our data platform: the pipelines that move healthcare data from customer EHR systems into Snowflake, and the models that turn that raw data into trusted core entities. You will work within workstreams scoped and architected with senior engineers on the team, and own your components end to end - building them, testing them, running them in production, and fixing them when they break. This is a growth role. You will get direct exposure to enterprise-scale healthcare data problems, a modern stack (Snowflake, SQLMesh, Temporal, Python, AWS, Datadog), and senior engineers invested in ramping you. The expectation is a steep trajectory: components today, workstreams as you grow. What You Will Do * Build and maintain pipeline components within larger workstreams, with architecture guidance from senior engineers. * Write production Python and SQL: extract logic, transformations, and data quality checks. * Contribute to our core entity models and learn the RCM domain that drives them. * Debug data quality and pipeline issues through to root cause, not just symptom. * Run what you build. You join the on-call rotation with support from senior engineers. * Develop AI-natively. AI coding tools (Claude Code and similar) are part of how this team designs, builds, and reviews. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Debugging in the Dark](https://www.wearedevelopers.com/videos/1658-debugging-in-the-dark) - [How building an industry DBMS differs from building a research one](https://www.wearedevelopers.com/videos/768-how-building-an-industry-dbms-differs-from-building-a-research-one) - [Why Your AI Agent Keeps Hallucinating Your Data: Building Deterministic Context Layers](https://www.wearedevelopers.com/videos/2055-why-your-ai-agent-keeps-hallucinating-your-data-building-deterministic-context-layers) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again)